Whole vehicle range extender power generation working condition point selection method and vehicle

Abstract

The application provides a whole vehicle range extender power generation working condition point selection method and a vehicle, and belongs to the technical field of range extender control. Wherein, according to vehicle information of a target vehicle and a current season, a target working condition point model corresponding to the target vehicle is acquired from a pre-constructed working condition point model library, the vehicle information includes: a target vehicle model of the target vehicle and a target region where the target vehicle currently locates, and the target working condition point model is used to indicate the corresponding relationship between the working condition of the target vehicle in the target region under the current season and the recommended power generation power; working condition data of the target vehicle is acquired in real time, and the working condition data includes: driving speed and remaining battery power; according to the working condition data and the target working condition point model, the recommended power generation power corresponding to the vehicle is determined, and the operation of the range extender in the vehicle is controlled according to the recommended power generation power. The application can achieve the effects of improving scene adaptability, improving the accuracy of working condition point selection and promoting the adaptive advancement of range extender working condition point selection strategy.

Description

Technical Field

[0001] This application relates to the field of range extender control technology, and more specifically, to a method for selecting the power generation operating point of a vehicle range extender and a vehicle. Background Technology

[0002] Range-extended electric vehicles (REEVs) are gradually becoming one of the mainstream choices for new energy vehicles due to their advantages such as low cost, high fuel efficiency ratio, and long driving range. Compared with traditional plug-in hybrid electric vehicles (PHEVs), REEVs achieve mechanical decoupling between the range extender and the wheels, allowing the range extender to operate independently in a more optimal operating range, thereby significantly improving overall energy utilization efficiency and reducing carbon emissions throughout the entire life cycle.

[0003] Currently, in the design and control strategy development of range-extended electric vehicles, the selection of the operating point is a crucial step in determining their actual energy efficiency. The "operating point" typically refers to the target power generation of the range extender under different vehicle speeds and load conditions. Existing methods for selecting the operating point of range extenders mainly rely on standardized driving cycle test procedures, such as the Worldwide Harmonized Light Vehicle Test Cycle (WLTC) or the China Light Vehicle Driving Cycle (CLTC), simulating the energy demands under these standard operating conditions to determine the design objectives and control logic of the range extender.

[0004] However, existing range extender operating point selection strategies have the following three shortcomings: First, there is the defect of scenario adaptation distortion. The operating points of range extenders set by relying on standardized driving cycle test procedures often deviate from the actual driving conditions of users, which will lead to insufficient utilization of the high-efficiency zone in actual operation of the range extender. Second, there is the defect of weak vehicle generalization ability. Range extender models with different displacements, power system configurations, or usage positioning have significantly different user driving behaviors and energy demand characteristics. If the data of multiple range extender models are mixed and clustered for unified analysis, it is easy to cause the centroid of data distribution to shift, thus affecting the accuracy of operating point extraction. Third, there is the defect of lacking a continuous iteration mechanism. After the operating points of range extender electric vehicles are determined in the R&D stage, they will be fixed in the vehicle controller. This means that a large amount of real range extender operation data accumulated after the vehicles are launched cannot be effectively fed back into the product iteration process, causing the range extender control strategy to stagnate. Summary of the Invention

[0005] The purpose of this application is to provide a method for selecting the power generation operating point of a vehicle range extender and a vehicle, which can improve the adaptability of the scenario, improve the accuracy of the operating point selection, and promote the adaptive advancement of the range extender operating point selection strategy.

[0006] The embodiments of this application are implemented as follows: A first aspect of this application provides a method for selecting the power generation operating point of a vehicle range extender, the method comprising: Based on the vehicle information of the target vehicle and the current season, the target operating point model corresponding to the target vehicle is obtained from the pre-built operating point model library. The vehicle information includes: the target vehicle model and the target area where the target vehicle is currently located. The target operating point model is used to indicate the correspondence between the operating conditions of the target vehicle in the target area in the current season and the recommended power generation. Real-time acquisition of operating data of the target vehicle, including driving speed and remaining battery power; Based on the operating condition data and the target operating point model, the recommended power generation capacity for the vehicle is determined, and the operation of the range extender in the vehicle is controlled according to the recommended power generation capacity.

[0007] As one possible implementation, the above-mentioned method for selecting the power generation operating point of the vehicle range extender also includes: Real-time acquisition of energy consumption information of the target vehicle, including: remaining battery charge, accelerator pedal opening and closing degree, and accessory power; Based on energy consumption information, the recommended power generation is adjusted to obtain the target power generation, and the operation of the range extender is controlled according to the target power generation.

[0008] As one possible implementation, the construction process of the above-mentioned working point model library is as follows: Within a preset sampling period, based on the vehicle ownership information of each vehicle model in each region, historical operating data of multiple vehicles corresponding to each vehicle model in each region are sampled, and the first statistical sample set corresponding to each vehicle model in each region is constructed. The historical operating data includes: vehicle speed, power generation and remaining battery power. The first statistical sample set for each region is divided according to the corresponding climate conditions, resulting in the second statistical sample set for each vehicle model in each region and season. According to the preset three-dimensional mapping relationship, cross statistics are performed on each second statistical sample set to generate multiple three-dimensional cross data combinations and multiple running mileage distribution matrices corresponding to each second statistical sample set. Based on multiple three-dimensional cross data combinations and multiple mileage distribution matrices corresponding to each second statistical sample set, a working condition point model corresponding to each vehicle type in each region and season is constructed, and the working condition point model is added to the working condition point model library.

[0009] As one possible implementation, cross-statistics are performed on the second statistical sample set according to a preset three-dimensional mapping relationship to generate multiple three-dimensional cross data combinations and multiple mileage distribution matrices, including: The second statistical sample is divided according to the preset vehicle speed range to obtain the third statistical sample corresponding to each vehicle speed range. The third statistical sample is divided according to the preset power range to obtain the fourth statistical sample corresponding to each power range; The fourth statistical sample is divided according to the preset correlation dimension to generate multiple three-dimensional cross data combinations and multiple running mileage distribution matrices.

[0010] As one possible implementation, the preset correlation dimensions include: a preset remaining battery capacity range, and dividing each fourth statistical sample according to the preset correlation dimensions to generate multiple three-dimensional cross-data combinations and multiple mileage distribution matrices, including: The percentage of operating mileage corresponding to each fourth statistical sample is statistically analyzed according to the preset battery remaining power range to obtain multiple three-dimensional cross data combinations and multiple operating mileage distribution matrices.

[0011] As one possible implementation, based on multiple three-dimensional cross-data combinations corresponding to each second statistical sample set and multiple mileage distribution matrices, a working condition model corresponding to each vehicle model in each region and season is constructed, and the working condition model is added to the working condition model library, including: From all the three-dimensional cross data combinations, the running mileage corresponding to the remaining battery charge range in different vehicle speed ranges within each power range is selected to form multiple running mileage distribution matrices. The median of each power interval contained in each power interval corresponding to each operating mileage distribution matrix is ​​weighted and averaged to obtain the recommended power generation power corresponding to each power interval. The comprehensive operating condition weighting coefficient is determined based on the operating mileage distribution matrix corresponding to each recommended power generation capacity. Based on the recommended power generation capacity and the comprehensive operating condition weighting coefficient, a model of the operating condition points for each vehicle type in each region and season is constructed.

[0012] As one possible implementation, a weighted average is performed on the median of the power intervals contained within each power interval corresponding to each operating mileage distribution matrix to obtain the recommended power generation for each power interval, including: Based on formula Determine the recommended power generation capacity for each power range; in, This refers to the recommended power generation capacity corresponding to each vehicle speed range. This refers to the vehicle's current power range being ( - The operating mileage of ), Pj is the current power range ( - The median of the power range is α, where α is the weighting coefficient for the remaining battery capacity.

[0013] As one possible implementation, the comprehensive operating condition weighting coefficient is determined based on the operating mileage distribution matrix corresponding to each recommended power generation capacity, including: Based on formula Determine the comprehensive working condition weighting coefficient; in, This refers to the comprehensive operating condition weighting coefficient. This refers to the total mileage of the vehicle. This refers to the vehicle's current power range being ( - ) running mileage.

[0014] As one possible implementation, a working point model is constructed based on the recommended power generation and the comprehensive operating condition weighting coefficient, including: The overall efficiency is determined based on the recommended power generation capacity and the weighting coefficient of the comprehensive operating conditions. Based on the overall efficiency, the theoretical operating point is determined, and an operating point model is constructed based on each theoretical operating point.

[0015] As one possible implementation, the overall efficiency is determined based on the recommended power generation capacity and the comprehensive operating condition weighting coefficient, including: Based on formula Determine the overall efficiency; in, Recommended power generation capacity The corresponding range extender power generation efficiency, It is the comprehensive working condition weighting coefficient. It refers to overall efficiency.

[0016] In a second aspect of this application, a vehicle is provided, comprising: a vehicle controller and a range extender, wherein the vehicle controller is configured to execute the steps of the vehicle range extender power generation operating point selection method described in the first aspect above, so as to control the operation of the range extender.

[0017] The beneficial effects of the embodiments of this application include: This application provides a method for selecting the power generation operating point of a vehicle range extender. The vehicle controller, based on the target vehicle model, the target region, and the current season, downloads a target operating point model matching the target vehicle from a pre-built operating point model library on a backend server via wireless communication technology. Based on the target operating point model, the recommended power generation for various operating conditions of the target vehicle in the current season and target region can be determined. Real-time data on the target vehicle's speed, remaining battery charge, and other operating conditions are collected, and the recommended power generation matching the current operating condition of the target vehicle is indexed from the target operating point model based on this real-time data. The operation of the range extender in the vehicle is controlled based on the recommended power generation given by the target operating point model. The target operating point model is obtained through data analysis of a large amount of real-world vehicle operating data of the same model as the target vehicle. The target operating point model covers all operating points of the target vehicle in the current region and season, avoiding the problem of traditional operating point selection strategies ignoring the relationship between temperature and accessory power, and also avoiding the problem of inaccurate operating point selection results caused by centroid deviation in data analysis results due to mixing data from different vehicle models. Furthermore, after the vehicle controller controls the range extender based on the recommended power output of the target operating point, it also feeds back the actual power output of the range extender in the target vehicle and the corresponding operating data to the backend server. The backend server can then use the data uploaded by the target vehicle to optimize the target operating point model, thereby advancing the range extender operating point selection strategy. This achieves the effects of improving scenario adaptability, increasing the accuracy of operating point selection, and promoting the adaptive advancement of the range extender operating point selection strategy. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 A flowchart illustrating the first method for selecting the power generation operating point of a vehicle range extender, as provided in this application embodiment; Figure 2 A flowchart illustrating the second method for selecting the power generation operating point of a vehicle range extender provided in this application embodiment; Figure 3 A flowchart illustrating the third method for selecting the power generation operating point of a vehicle range extender provided in this application embodiment; Figure 4 A statistical result graph of a first statistical sample set provided for an embodiment of this application; Figure 5 A statistical result graph of a second statistical sample set provided for an embodiment of this application; Figure 6 A flowchart illustrating the fourth method for selecting the power generation operating point of a vehicle range extender provided in this application embodiment; Figure 7 A statistical result diagram of three-dimensional cross-data combination provided in an embodiment of this application; Figure 8 A flowchart illustrating the fifth method for selecting the power generation operating point of a vehicle range extender provided in this application embodiment; Figure 9 A simplified rule table for operating points is provided for embodiments of this application; Figure 10 A flowchart illustrating the sixth method for selecting the power generation operating point of a vehicle range extender provided in this application embodiment; Figure 11 This is a structural schematic diagram of a vehicle provided in an embodiment of this application.

[0020] Reference numerals: 11: Vehicle; 1101: Vehicle controller; 1102: Range extender. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0023] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0024] Currently, the selection strategy for the power generation operating point of vehicle range extenders mainly relies on standardized driving cycle test procedures, which determine the design goals and control logic of the range extender by simulating the energy demand under standard operating conditions. However, this strategy for selecting the power generation operating point of range extenders has the following drawbacks: distorted scenario adaptation, weak vehicle model generalization ability, and lack of continuous iteration based on user operating conditions.

[0025] Therefore, there is an urgent need for a dynamic optimization strategy for range extender operating points based on real-world vehicle usage scenarios, in order to promote the development of range-extended electric vehicles towards higher energy efficiency, stronger adaptability, and greater intelligence.

[0026] To address this, this application provides a method for selecting the power generation operating point of a vehicle range extender. This method involves acquiring vehicle information and the current season of the target vehicle, and downloading a target operating point model corresponding to the target vehicle from a pre-built operating point model library. Real-time operating data of the target vehicle is collected, and a recommended power generation capacity matching the real-time operating data is indexed from the target operating point model. The recommended power generation capacity is adjusted based on the real-time energy consumption information of the target vehicle to generate the corresponding target power generation capacity. The operation of the range extender in the target vehicle is then controlled according to the target power generation capacity. The vehicle information includes the target vehicle model and the target area currently occupied by the target vehicle. The target operating point model indicates the correspondence between various operating conditions and recommended power generation capacities when the target vehicle is in the target area during the current season.

[0027] Therefore, the operating point model provided in this application is compatible with all regions of the country, all seasons of the year, and all vehicle models. It can avoid the error of range extender operating data in a single region, avoid the problem of traditional operating point selection models ignoring the relationship between temperature and energy consumption, and avoid mixing range extender operating data from different vehicle models.

[0028] In this way, we can improve the adaptability of the scenario, improve the accuracy of the operating point selection, and promote the adaptive advancement of the operating point selection strategy of the range extender.

[0029] The technical solution of this application can be applied to range-extended electric vehicles.

[0030] The method for selecting the power generation operating point of the vehicle range extender provided in this application will be explained in detail below with reference to the accompanying drawings.

[0031] Figure 1 This application provides a flowchart of a method for selecting the power generation operating point of a vehicle range extender. This method is applied to the vehicle controller in a vehicle, specifically the vehicle controller in a range-extended electric vehicle. See also... Figure 1 This application provides a method for selecting the power generation operating point of a vehicle range extender, including: S101. Based on the vehicle information of the target vehicle and the current season, obtain the target operating point model corresponding to the target vehicle from the pre-built operating point model library. The vehicle information includes: the target vehicle model and the target area where the target vehicle is currently located. The target operating point model is used to indicate the correspondence between the operating condition of the target vehicle in the target area in the current season and the recommended power generation.

[0032] The pre-built operating point model library is a database of operating point models for various vehicle models, based on massive data analysis results from real-world vehicle usage scenarios, including range extender operation data, vehicle speed, mileage, and remaining battery charge. The target operating point model refers to the operating point model that matches the target vehicle's model, current season, and current location.

[0033] It is worth noting that the pre-built working condition point model library stores working condition point models corresponding to various vehicle models. Each vehicle model downloads a working condition point model that matches its own model from the pre-built working condition point model library based on its own model, current season, and current location.

[0034] Optionally, the target vehicle refers to any one of several vehicle types. The vehicle information of the target vehicle is used to measure the current location of the target vehicle and its inherent vehicle type. The vehicle information includes: the target vehicle type and the target region where the target vehicle is currently located. Here, the target vehicle type refers to the vehicle model, the target region refers to the region where the target vehicle is currently located, and the current season refers to the current season of the target vehicle.

[0035] Optionally, the current season can be any of spring, summer, autumn, or winter, and the target region can be any of the following regions: North China, Northeast China, East China, Central and Southern China, Northwest China, and Southwest China. This application does not impose any specific restrictions on this.

[0036] Optionally, the target vehicle loads a target operating condition model from a pre-built operating condition model library based on its vehicle model, current location, and current season. The vehicle controller, based on the target operating condition model, can determine the recommended power generation for various operating conditions of the target vehicle in the current season and current location. In other words, the target operating condition model can determine a pre-set operating condition map for the target vehicle, with each operating condition having its corresponding recommended power generation.

[0037] It is worth noting that the recommended power generation power refers to the range extender power generation power directly indexed by the target operating point model based on the actual vehicle operating data of the target vehicle. In other words, the recommended power generation power is the empirical power generation power corresponding to various operating conditions of the target vehicle. Theoretically, the recommended power generation power can enable the range extender to operate efficiently under the current operating conditions of the target vehicle.

[0038] S102. Real-time acquisition of the target vehicle's operating condition data, including: vehicle speed and remaining battery power.

[0039] Optionally, the vehicle controller acquires real-time operating condition data of the target vehicle from various sensors deployed in the target vehicle via a data bus. The real-time operating condition data of the target vehicle includes the vehicle speed and the remaining battery charge. The vehicle speed is the forward speed of the target vehicle at the current moment, and the remaining battery charge is the amount of charge remaining in the target vehicle's battery at the current moment.

[0040] Specifically, the driving speed can be 30km / h, 50km / h, etc., and this application does not make a specific limitation on it.

[0041] It is worth noting that the remaining battery capacity is usually measured as a percentage between the current battery capacity and the battery's rated capacity, such as 20%, 30%, 80%, 100%, etc. This application does not make any specific limitation on this.

[0042] S103. Based on the operating condition data and the target operating point model, determine the recommended power generation capacity for the vehicle, and control the operation of the range extender in the vehicle according to the recommended power generation capacity.

[0043] Optionally, a recommended power generation capacity matching the real-time operating data of the target vehicle can be indexed from the target operating point model, and the operation of the range extender in the vehicle can be controlled based on the recommended power generation capacity. It should be noted that the target operating point model records the correspondence between various operating conditions of the target vehicle and the recommended power generation capacity.

[0044] It should be noted that the range extender is the core power component of a range-extended electric vehicle. The range extender is equivalent to an on-board generator set. That is, the range extender is a combination of an engine and a generator. The engine in the range extender is only used to drive the generator to generate electricity and has no mechanical connection with the vehicle's wheels.

[0045] Specifically, the core purpose of a range extender is to eliminate range anxiety and improve the overall energy efficiency of the vehicle. The electricity generated by the range extender can not only directly power the vehicle's drive motor but also charge the vehicle's battery, thereby improving the battery's state of charge. At the same time, the range extender can also ensure that the vehicle's engine always operates in its most efficient range, allowing the engine to be controlled to consistently run within a preset, most efficient speed and load range.

[0046] In addition, the performance of vehicle batteries will degrade in extreme low-temperature environments. Range extenders can not only provide power to the vehicle, but the waste heat generated by the engine can also be used to provide heat to the cabin and vehicle battery, thereby reducing battery power consumption and ensuring the vehicle's range in low-temperature environments.

[0047] In this embodiment, the vehicle controller downloads a target operating condition model matching the target vehicle from a pre-built operating condition model library on a backend server via wireless communication technology, based on the target vehicle model, the target area where the target vehicle is currently located, and the current season. Based on the target operating condition model, the controller can determine the recommended power generation for various operating conditions of the target vehicle in the current season and target area. It also collects real-time operating condition data such as the vehicle's speed and remaining battery power, and indexes the recommended power generation matching the current operating condition of the target vehicle from the target operating condition model based on this real-time data. Finally, it controls the operation of the range extender in the vehicle based on the recommended power generation given by the target operating condition model. The target operating condition model is obtained through data analysis of a large amount of real-world vehicle operating data of the same model as the target vehicle. This model covers all operating conditions of the target vehicle in the current area and season, avoiding the problem of traditional operating condition selection strategies ignoring the relationship between temperature and accessory power, and also avoiding the problem of inaccurate operating condition selection results caused by centroid deviation in data analysis results due to mixing data from different vehicle models. Furthermore, after the vehicle controller controls the range extender based on the recommended power output of the target operating point, it also feeds back the actual power output of the range extender in the target vehicle and the corresponding operating data to the backend server. The backend server can then use the data uploaded by the target vehicle to optimize the target operating point model, thereby advancing the range extender operating point selection strategy. This achieves the effects of improving scenario adaptability, increasing the accuracy of operating point selection, and promoting the adaptive advancement of the range extender operating point selection strategy.

[0048] In one alternative implementation, see [link to implementation details]. Figure 2 The method for selecting the power generation operating point of a vehicle range extender provided in this application embodiment further includes: S201. Real-time acquisition of energy consumption information of the target vehicle, including: remaining battery charge, accelerator pedal opening and closing degree, and accessory power.

[0049] Optionally, energy consumption information refers to the energy consumption information generated by the vehicle in actual operating scenarios. This energy consumption information includes: remaining battery charge, accelerator pedal opening degree, and accessory power. Specifically, remaining battery charge measures the real-time remaining charge of the vehicle's battery, which can be measured as a percentage of the battery's rated capacity, such as 20%, 80%, or 100%. Accelerator pedal opening degree measures the driver's driving power demand; when the driver presses the accelerator pedal hard, the opening degree is larger, and the driver's driving power demand increases sharply. Accessory power measures the power consumed by other loads in the vehicle. Accessory power can be the power consumption of the vehicle's air conditioning or other loads; this application does not specifically limit this.

[0050] Specifically, the vehicle controller can obtain the real-time remaining power of the vehicle battery through the battery management system, obtain the accelerator pedal opening degree through sensors deployed on the accelerator pedal, and obtain the accessory power through the air conditioning controller or other controllers.

[0051] S202. Based on energy consumption information, adjust the recommended power generation to obtain the target power generation, and control the operation of the range extender according to the target power generation.

[0052] The target power generation refers to the final power generation obtained by adjusting the recommended power generation of the target operating point model based on the real-time energy consumption information of the target vehicle. In other words, the target power generation is the range extender power generation that matches the real-time operating conditions of the target vehicle during actual vehicle operation.

[0053] Optionally, the vehicle controller corrects the recommended power generation given by the target operating point model based on energy consumption information to obtain a target power generation that is compatible with the actual vehicle usage scenario, and controls the operation of the range extender in the vehicle based on the corrected target power generation.

[0054] Specifically, the real-time remaining battery power serves as the basis for closed-loop correction of the target power generation. When the real-time remaining battery power is low, the recommended power generation given by the target operating point model needs to be adjusted upward to prevent the battery from running out of power. When the real-time remaining battery power is high, the recommended power generation given by the target operating point model can be adjusted downward, or the range extender can be delayed to prioritize the use of energy provided by the vehicle battery, thereby saving resources.

[0055] Furthermore, the accelerator pedal opening degree serves as a feedforward signal for correcting the recommended power generation. When the driver presses the accelerator pedal hard, it indicates a sharp increase in the vehicle's drive power demand. The vehicle controller needs to increase the range extender's power command in advance and quickly to work with the battery to meet the vehicle's power demand and avoid drastic fluctuations in the battery's remaining charge. The accessory power serves as load compensation for correcting the recommended power generation. Under extreme temperature conditions, the power consumption of the air conditioning for heating or cooling is extremely high. This power consumption needs to be added to the recommended power generation in real time to ensure that the range extender's output power can cover all the vehicle's energy consumption.

[0056] It should also be noted that the vehicle controller also collects road information provided by the navigation system and uses this information as a forward-looking predictive signal to adjust the recommended power generation. When the road information indicates a long uphill section ahead, the vehicle controller needs to increase the power generation of the range extender in advance to establish a State of Charge (SOC) buffer. When the road information indicates a long downhill section ahead, the vehicle controller needs to reduce the power generation of the range extender in advance and prioritize the use of regenerative braking for power supply.

[0057] Optionally, the vehicle controller corrects the recommended power generation given by the target operating point model using a closed-loop proportional-integral-derivative (PID) controller based on the real-time remaining battery power, and performs feedforward and load compensation calculations on the recommended power generation based on the accelerator pedal opening degree, road slope information, and accessory power to obtain the final target power generation.

[0058] In one alternative implementation, see [link to implementation details]. Figure 3 The construction process of the operating point model library in the method for selecting the power generation operating point of the vehicle range extender provided in this application embodiment is as follows: S301. Within the preset sampling period, based on the vehicle ownership information of each vehicle model in each region, sample the historical operating data of multiple vehicles corresponding to each vehicle model in each region, and construct the first statistical sample set corresponding to each vehicle model in each region. The historical operating data includes: vehicle speed, power generation and remaining battery power.

[0059] The preset sampling period is a data sampling period set by the user in advance. The preset sampling period can be one year, three years, etc., and this application does not make a specific limitation on it.

[0060] Optionally, vehicle ownership information includes multi-dimensional data such as vehicle model distribution, brand distribution, and regional distribution. Vehicle ownership information refers to information on cars that have been registered and are legally driving on the road in a specific region at a specific point in time, such as the total number of each type of vehicle, the total number of each brand, and the number of vehicles in each region.

[0061] Optionally, based on the vehicle ownership information of each vehicle type in various regions across the country, historical operating data of multiple vehicles corresponding to each vehicle type in each region are sampled, and a first statistical sample set corresponding to each vehicle type in each region is constructed based on the sampled historical operating data. Here, historical operating data refers to the actual vehicle operating data of each type of vehicle in various regions across the country within a preset sampling period; the first statistical sample set is a set of statistical data obtained by statistically analyzing the sampled historical operating data according to regional division rules.

[0062] Specifically, historical operating data includes: vehicle speed, power generation, and remaining battery charge. Vehicle speed refers to the driving speed of various types of vehicles in different regions of the country within the preset sampling period; power generation refers to the power generation of the range extender under various operating conditions for various types of vehicles within the preset sampling period; and remaining battery charge refers to the remaining battery charge for various types of vehicles under various operating conditions within the preset sampling period.

[0063] It is worth noting that each type of vehicle has its own corresponding first statistical sample set in each region.

[0064] In one alternative implementation, see [link to implementation details]. Figure 4 The country is divided into six regions: North China, Northeast China, East China, Central and Southern China, Southwest China, and Northwest China. Historical operational data for N vehicles of target model X are sampled from these regions over one year. Specifically, the sales volume of target model X vehicles in North China is *a* ten thousand units, in Northeast China it is *b* ten thousand units, in East China it is *c* ten thousand units, in Central and Southern China it is *d* ten thousand units, in Southwest China it is *e* ten thousand units, and in Northwest China it is *f* ten thousand units. Therefore, the vehicle allocation ratio for target model X vehicles in North China is determined as A = a / (a+b+c+d+e+f), and the number of target model X vehicles sampled in North China is N1 = A × N. Similarly, the vehicle allocation ratio for target model X vehicles in Northeast China is determined as B = b / (a+b+c+d+e+f), and the number of target model X vehicles sampled in Northeast China is determined as B. N2 = B × N; The vehicle allocation ratio of target model X in East China is C = c / (a+b+c+d+e+f), and the number of target model X vehicles sampled in East China is N3 = C × N; The vehicle allocation ratio of target model X in Central and Southern China is D = d / (a+b+c+d+e+f), and the number of target model X vehicles sampled in Central and Southern China is N4 = D × N; The vehicle allocation ratio of target model X in Southwest China is determined to be E = e / (a+b+c+d+e+f), and the number of target model X vehicles sampled in Southwest China is N5 = E × N; The vehicle allocation ratio of target model X in Northwest China is F = f / (a+b+c+d+e+f), and the number of target model X vehicles sampled in Northwest China is N1 = F × N.

[0065] Therefore, the historical operating data of N1 vehicles of target model X in North China serves as a first statistical sample; the historical operating data of N2 vehicles of target model X in Northeast China also serves as a first statistical sample; the historical operating data of N3 vehicles of target model X in East China also serves as a first statistical sample; the historical operating data of N4 vehicles of target model X in Central and Southern China also serves as a first statistical sample; the historical operating data of N5 vehicles of target model X in Southwest China also serves as a first statistical sample; and the historical operating data of N6 vehicles of target model X in Northwest China also serves as a first statistical sample.

[0066] S302. Divide the first statistical sample set of each region according to the corresponding climate conditions of each region to obtain the second statistical sample set of each vehicle type in each season of each region.

[0067] Optionally, by dividing the first statistical sample set for each region according to the corresponding climatic conditions, a second statistical sample set for each vehicle type in each season of each region can be obtained. The second statistical sample set is a collection of statistical data obtained by dividing and statistically analyzing the first statistical sample set for each vehicle type in each region according to seasonal division rules. It is worth noting that each type of vehicle has its own corresponding second statistical sample set for each season in each region; that is, each type of vehicle has four separate statistical sample sets for each season in each region.

[0068] In one alternative implementation, see [link to implementation details]. Figure 5 By dividing the historical operating data of N1 vehicles with target model X sampled in North China according to spring, summer, autumn, and winter, we can obtain the historical operating data of n11 vehicles with target model X in North China during spring, n12 vehicles with target model X in North China during summer, n13 vehicles with target model X in North China during autumn, and n14 vehicles with target model X in North China during winter. The historical operating data of n11 vehicles with target model X in North China during spring, n12 vehicles with target model X in North China during summer, n13 vehicles with target model X in North China during autumn, and n14 vehicles with target model X in North China during winter constitute a second statistical sample set.

[0069] Optionally, dividing the historical operating data of various types of vehicles in different regions according to spring, summer, autumn, and winter can avoid the problem of low-temperature energy efficiency degradation and ensure high adaptability of the range extender's power generation operating point. For example, the second statistical sample set corresponding to spring can reflect the difference in range extender operating conditions between mild and extreme temperatures, the second statistical sample set corresponding to summer can reflect the range extender operating conditions in high-temperature scenarios, the second statistical sample set corresponding to autumn can reflect the operating patterns of the range extender at lower temperatures, and the second statistical sample set corresponding to winter can reflect the start-up characteristics of the range extender under extreme low-temperature environments, etc.

[0070] S303. Perform cross-statistics on each second statistical sample set according to the preset three-dimensional mapping relationship to generate multiple three-dimensional cross data combinations and multiple running mileage distribution matrices corresponding to each second statistical sample set.

[0071] Optionally, the preset three-dimensional mapping relationship is a three-dimensional mapping relationship preset by the user. Specifically, the preset three-dimensional mapping relationship can be a mapping relationship of vehicle speed range - power range - running mileage ratio. This application does not make specific limitations on this.

[0072] Optionally, by performing cross-statistics on multiple second statistical sample sets according to a preset three-dimensional mapping relationship, multiple three-dimensional cross-data combinations and multiple mileage distribution matrices corresponding to each second statistical sample set can be obtained. The three-dimensional cross-data combinations reflect the cross-statistical results between vehicle speed, power, and remaining battery charge for each type of vehicle in different regions and seasons. The mileage distribution matrix reflects the mileage traveled by each type of vehicle in different regions and seasons within different speed ranges corresponding to different power ranges, and uses this percentage of the total mileage as the mileage distribution statistical result.

[0073] S304. Based on the multiple three-dimensional cross data combinations and multiple mileage distribution matrices corresponding to each second statistical sample set, construct the working condition point model corresponding to each vehicle type in each region and season, and add the working condition point model to the working condition point model library.

[0074] Optionally, based on multiple three-dimensional cross data combinations and multiple mileage distribution matrices corresponding to each second statistical sample set, a working condition model corresponding to each vehicle model in each region and each season is generated, and each working condition model is added to a pre-built working condition model library.

[0075] In one alternative implementation, see [link to implementation details]. Figure 6 The specific operation of step S303 above can be as follows: S601. Divide the second statistical sample according to the preset vehicle speed range to obtain the third statistical sample corresponding to each vehicle speed range.

[0076] Optionally, the preset speed range is a speed division range set by the user in advance. The preset speed range can be 10km / h-30km / h, 30km / h-50km / h, 90km / h-120km / h, etc. This application does not make specific limitations on this.

[0077] Optionally, by dividing the second statistical sample set corresponding to each vehicle model in each region and season according to the speed range predefined by the user, a third statistical sample can be obtained. This third statistical sample reflects the speed distribution of each vehicle model in each region and season.

[0078] S602. Divide each third statistical sample according to the preset power range to obtain the fourth statistical sample corresponding to each power range.

[0079] Optionally, the preset power range is a range of ranges defined by the user's pre-set power output of the range extender. The preset power range can be 10kW-20kW, 20kW-30kW, etc., and this application does not make any specific limitation on it.

[0080] Optionally, a fourth statistical sample can be obtained by dividing the third statistical sample set corresponding to each vehicle model in each region, season, and speed range according to the range extender power generation range predefined by the user. This fourth statistical sample reflects the distribution of range extender power generation for each vehicle model in each region, season, and speed range.

[0081] S603. Divide each fourth statistical sample according to the preset correlation dimension to generate multiple three-dimensional cross data combinations and multiple running mileage distribution matrices.

[0082] Optionally, the preset correlation dimension refers to the correlation between the remaining battery power and the running mileage. The preset correlation dimension includes: the preset remaining battery power range, which is a power range predefined by the user. The preset remaining battery power range can be 0%-80%, 80%-100%, etc. This application does not make specific limitations on this.

[0083] Optionally, according to the user-defined battery capacity ranges, the fourth statistical sample set corresponding to each vehicle model in each region, season, speed range, and power range can be divided to obtain multiple three-dimensional cross-data combinations. These three-dimensional cross-data combinations reflect the distribution of the operating mileage percentage for each vehicle model in each region, season, speed range, power range, and remaining battery capacity range.

[0084] In one alternative implementation, see [link to implementation details]. Figure 7 ,according to , … By dividing the historical operating data of n11 vehicles with target model X sampled in North China during spring into equal speed intervals, we can obtain the historical operating data of multiple vehicles with target model X corresponding to each speed interval in North China during spring; then according to... , … The equal power range will be the first speed range. By dividing the historical operating data of multiple target models X, we can obtain the historical operating data of multiple target models X in the first speed range and power range in North China during spring. Finally, we can statistically analyze the operating mileage of multiple target models X in the first speed range and power range in North China during spring according to the remaining battery charge range, so as to obtain multiple three-dimensional cross data combinations.

[0085] In one optional implementation, the preset association dimension includes: a preset remaining battery power range, and the operation of step S603 above can specifically be: The percentage of operating mileage corresponding to each fourth statistical sample is statistically analyzed according to the preset battery remaining power range to obtain multiple three-dimensional cross data combinations and multiple operating mileage distribution matrices.

[0086] Optionally, the mileage percentage refers to the proportion of the vehicle's mileage to the total mileage when the vehicle is traveling at the current speed in the current season in the current region and the range extender is generating electricity within the current power range.

[0087] Optionally, according to the preset remaining battery power range, the fourth statistical sample of each vehicle model is statistically analyzed. When the vehicle is traveling at a speed limited by the current speed range in the current season of the currently mentioned region, and the range extender generates electricity at the power limited by the current power generation range corresponding to the current speed range, the proportion of the vehicle's mileage to the total mileage of the vehicle within the preset sampling period is calculated. This yields a three-dimensional cross data combination of speed range - power range - remaining battery power range (mileage percentage). Each three-dimensional cross data combination contains the mileage distribution of each vehicle model.

[0088] In one alternative implementation, see [link to implementation details]. Figure 8 The specific operation of step S304 above can be as follows: S801. Select the running mileage of the remaining battery charge range corresponding to different vehicle speed ranges within each power range from all three-dimensional cross data combinations to form multiple running mileage distribution matrices.

[0089] Optionally, the percentage of running mileage corresponding to each power range and each remaining battery charge range in each speed range of all three-dimensional cross data combinations is traversed to determine the cumulative percentage of running mileage in different speed ranges and different remaining battery charge ranges for each power range, so as to form multiple running mileage distribution matrices.

[0090] Optionally, one or more power ranges with the largest percentage of operating mileage can be selected from multiple operating mileage distribution matrices and defined as preset high-frequency operating ranges.

[0091] S802. Perform a weighted average of the median values ​​of the power intervals contained in each power interval corresponding to each operating mileage distribution matrix to obtain the recommended power generation power corresponding to each power interval.

[0092] Optionally, by performing a weighted average of the power generation within each power interval corresponding to each operating mileage distribution matrix, the recommended power generation for each power interval can be obtained.

[0093] S803. Determine the comprehensive operating condition weighting coefficient based on the operating mileage distribution matrix corresponding to each recommended power generation capacity.

[0094] Optionally, based on the operating mileage distribution matrix corresponding to each recommended power generation capacity, a comprehensive operating condition weighting coefficient is calculated. The comprehensive operating condition weighting coefficient is used to evaluate the importance of each recommended power generation capacity in the user's actual vehicle operation scenario.

[0095] S804. Based on the recommended power generation capacity and the comprehensive operating condition weighting coefficient, construct the operating condition point model for each vehicle type in each region and season.

[0096] Optionally, based on each recommended power generation and the comprehensive operating condition weighting coefficient, the comprehensive efficiency corresponding to each recommended power generation is determined. This efficiency is used to evaluate the overall average efficiency of the recommended power generation in the user's actual vehicle operation scenario before the range extender is put into operation. Based on the operating condition points with higher comprehensive efficiency, operating condition point models corresponding to each vehicle model in each region and season are constructed.

[0097] In one alternative implementation, see [link to implementation details]. Figure 9 Vehicle speed is an indicator of the vehicle's driving status, and the vehicle speed is generally taken as the median of the speed range; power generation is the core output indicator of the range extender, which is obtained by weighted averaging of the power range; the correlation dimension is used to characterize the battery load status, and the recommended power generation given by the target operating point model is corrected based on the battery load status.

[0098] In one optional implementation, the recommended power generation capacity corresponding to each power range can be determined according to the following formula (1), which is as follows: (1) in, This refers to the recommended power generation capacity corresponding to each vehicle speed range. This refers to the vehicle's current power range being ( - The operating mileage of ), Pj is the current power range ( - The median of the power range is α, where α is the weighting coefficient for the remaining battery capacity.

[0099] Optionally, based on formula (1), a recommended power generation capacity that best represents the user's actual needs and is most friendly to battery health can be calculated within a specific power range.

[0100] Among them, the larger the value of the running mileage, the more it represents the user's performance within the current power range ( - The more vehicles are used, the more important this power range becomes, ensuring that the final recommended power generation is closer to the power generation range at the operating point that the user relies on; the median of the power range refers to the current power range ( - The baseline representative value is the battery remaining power; the weighting coefficient of the battery remaining power is the loss coefficient of the battery remaining power corresponding to the data point of recommended power generation.

[0101] Alternatively, even if a user frequently uses a certain power output point, but always does so when the battery's remaining charge is unhealthy, this usage habit is unsustainable or inefficient. The operating point model will automatically adjust the recommended power output point towards those operating points that both meet the user's needs and allow operation within a healthy and efficient battery charge range.

[0102] In one optional implementation, the comprehensive working condition weighting coefficient can be determined according to the following formula (2), which is as follows: (2) in, This refers to the comprehensive operating condition weighting coefficient. This refers to the total mileage of the vehicle. This refers to the vehicle's current power range being ( - ) running mileage.

[0103] Optionally, the importance of each recommended power generation capacity in the actual operation of the user's vehicle can be evaluated based on formula (2). It measures the total distance the range extender travels at its generated power output Pj across all vehicle speed ranges, and is used to measure the total time this generated power output is used during the user's driving. It is the total mileage driven across all speed ranges and all power ranges, representing the complete set of user vehicle usage.

[0104] Specifically, for A high recommended generating capacity means that users rely on this power point most of the time, and must devote maximum effort to ensuring the range extender's efficiency is at its peak at this point. Conversely, for... Even if the value is very low, its impact on overall energy consumption is negligible, and less optimization resources can be allocated.

[0105] In one alternative implementation, see [link to implementation details]. Figure 10 The specific operation of step S804 above can be as follows: S1001. Determine the overall efficiency based on the recommended power generation capacity and the comprehensive operating condition weighting coefficient.

[0106] Optionally, the overall efficiency of the recommended power generation for the vehicle operation can be calculated based on each recommended power generation and the weighting coefficient of the comprehensive operating condition.

[0107] S1002. Based on the overall efficiency, determine the theoretical operating point and construct the operating point model based on each theoretical operating point.

[0108] Optionally, based on the overall efficiency, the recommended power generation corresponding to the theoretically efficient operating point is determined, and an operating point model is constructed based on the recommended power generation corresponding to each theoretical operating point.

[0109] In one optional implementation, the overall efficiency can be determined according to the following formula (3), which is as follows: (3) in, Recommended power generation capacity The corresponding range extender power generation efficiency, It is the comprehensive working condition weighting coefficient. It refers to overall efficiency.

[0110] Optionally, based on formula (3), the global average efficiency of each recommended power generation in the user's actual vehicle use scenario is predicted, which provides a forward-looking evaluation index that is highly consistent with actual use.

[0111] In an optional implementation, the above-mentioned method for selecting the power generation operating point of the vehicle range extender further includes: The system collects real-time operating data of the target vehicle and iteratively optimizes the operating point model based on the actual operating data.

[0112] Optionally, the actual operating data of the target vehicle can be fed back to the backend server. The backend server analyzes and statistically analyzes the real-time operating data of the target vehicle to provide feedback to the target operating point model corresponding to the target vehicle.

[0113] Figure 11 A structural schematic diagram of a vehicle provided in this application is shown below. Figure 11 The vehicle 11 provided in this application embodiment includes: a vehicle controller 1101 and a range extender 1102, wherein the vehicle controller 1101 is connected to the range extender 1102, and the vehicle controller 1101 is written with the software program of the above-mentioned vehicle range extender discharge operating point selection method. When the vehicle controller 1101 runs the vehicle range extender power generation operating point selection method, it controls the operation of the range extender 1102 in the vehicle.

[0114] When the vehicle controller 1101 runs the software program for the method of selecting the discharge operating point of the vehicle range extender, the specific implementation process and technical effects of the software program are the same as those in the above embodiments, and will not be repeated here.

[0115] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0116] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for selecting the power generation operating point of a vehicle range extender, characterized in that, The method includes: Based on the vehicle information of the target vehicle and the current season, the target operating condition model corresponding to the target vehicle is obtained from the pre-built operating condition model library. The vehicle information includes: the target vehicle model and the target region where the target vehicle is currently located. The target operating condition model is used to indicate the correspondence between the operating condition of the target vehicle and the recommended power generation when the target vehicle is in the target region in the current season. Real-time acquisition of the target vehicle's operating condition data, including: vehicle speed and remaining battery power; Based on the operating condition data and the target operating point model, the recommended power generation power corresponding to the vehicle is determined, and the operation of the range extender in the vehicle is controlled according to the recommended power generation power. The construction process of the working condition point model library is as follows: Within a preset sampling period, based on the vehicle ownership information of each vehicle model in each region, historical operating data of multiple vehicles corresponding to each vehicle model in each region are sampled, and a first statistical sample set corresponding to each vehicle model in each region is constructed. The historical operating data includes: vehicle speed, power generation and remaining battery power. The first statistical sample set for each region is divided according to the corresponding climate conditions, resulting in the second statistical sample set for each vehicle model in each region and season. According to the preset three-dimensional mapping relationship, cross statistics are performed on each second statistical sample set to generate multiple three-dimensional cross data combinations and multiple running mileage distribution matrices corresponding to each second statistical sample set. Based on multiple three-dimensional cross data combinations and multiple mileage distribution matrices corresponding to each second statistical sample set, construct the working condition point model corresponding to each vehicle type in each region and each season, and add the working condition point model to the working condition point model library. The step involves constructing operating condition models for each vehicle model in different regions and seasons based on multiple three-dimensional cross-data combinations and multiple mileage distribution matrices corresponding to each second statistical sample set, and adding these operating condition models to the operating condition model library. From all the three-dimensional cross data combinations, the running mileage corresponding to the remaining battery charge range in different vehicle speed ranges within each power range is selected to form multiple running mileage distribution matrices. The median of each power interval contained in each power interval corresponding to each operating mileage distribution matrix is ​​weighted and averaged to obtain the recommended power generation power corresponding to each power interval. The comprehensive operating condition weighting coefficient is determined based on the operating mileage distribution matrix corresponding to each recommended power generation capacity. Based on the recommended power generation capacity and the comprehensive operating condition weighting coefficient, a model of the operating condition points for each vehicle type in each region and season is constructed.

2. The method for selecting the power generation operating point of a vehicle range extender according to claim 1, characterized in that, The method further includes: Real-time acquisition of energy consumption information of the target vehicle, including: remaining battery charge, accelerator pedal opening degree, and accessory power; Based on the energy consumption information, the recommended power generation is adjusted to obtain the target power generation, and the operation of the range extender is controlled according to the target power generation.

3. The method for selecting the power generation operating point of a vehicle range extender according to claim 1, characterized in that, The step of performing cross-statistics on the second statistical sample set according to a preset three-dimensional mapping relationship to generate multiple three-dimensional cross-data combinations and multiple mileage distribution matrices includes: The second statistical sample is divided according to the preset vehicle speed range to obtain the third statistical sample corresponding to each vehicle speed range; The third statistical samples are divided according to the preset power range to obtain the fourth statistical samples corresponding to each power range. The fourth statistical samples are divided according to the preset correlation dimensions to generate multiple three-dimensional cross data combinations and multiple running mileage distribution matrices.

4. The method for selecting the power generation operating point of a vehicle range extender according to claim 3, characterized in that, The preset correlation dimension includes: a preset remaining battery power range. The step of dividing each of the fourth statistical samples according to the preset correlation dimension to generate multiple three-dimensional cross-data combinations and a running mileage distribution matrix corresponding to each three-dimensional cross-data combination includes: The percentage of operating mileage corresponding to each of the fourth statistical samples is statistically analyzed according to the preset remaining battery power range to obtain multiple three-dimensional cross data combinations and multiple operating mileage distribution matrices.

5. The method for selecting the power generation operating point of a vehicle range extender according to claim 1, characterized in that, The step of performing a weighted average of the median values ​​of the power intervals contained in each power interval corresponding to each operating mileage distribution matrix to obtain the recommended power generation for each power interval includes: Based on formula Determine the recommended power generation capacity for each power range; in, This refers to the recommended power generation capacity corresponding to each power range. This refers to the vehicle's current power range being ( - The operating mileage of ), Pj is the current power range ( - The median of the power range is α, where α is the weighting coefficient for the remaining battery capacity.

6. The method for selecting the power generation operating point of a vehicle range extender according to claim 1, characterized in that, The determination of the comprehensive operating condition weighting coefficient based on the operating mileage distribution matrix corresponding to each recommended power generation includes: Based on formula Determine the comprehensive working condition weighting coefficient; in, This refers to the comprehensive operating condition weighting coefficient. This refers to the total mileage of the vehicle. This refers to the vehicle's current power range being ( - ) running mileage.

7. The method for selecting the power generation operating point of a vehicle range extender according to claim 1, characterized in that, The step of constructing the operating point model based on each recommended power generation capacity and the comprehensive operating condition weighting coefficient includes: The overall efficiency is determined based on the recommended power generation capacity and the weighting coefficient of the comprehensive operating condition. Based on the overall efficiency, the theoretical operating point is determined, and an operating point model is constructed based on each theoretical operating point.

8. The method for selecting the power generation operating point of a vehicle range extender according to claim 7, characterized in that, The determination of overall efficiency based on each recommended power generation capacity and the overall operating condition weighting coefficient includes: Based on formula Determine the overall efficiency; in, Recommended power generation capacity The corresponding range extender power generation efficiency, It is the comprehensive working condition weighting coefficient. It refers to overall efficiency.

9. A vehicle, characterized in that, The vehicle includes a vehicle controller and a range extender, wherein the vehicle controller is used to execute the steps of the vehicle range extender power generation operating point selection method according to any one of claims 1 to 8, so as to control the operation of the range extender.

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